Every paper checked — nothing hidden

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Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an imitation learning method, not an LLM benchmark.
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses general imitation learning theory rather than LLM-based agents.
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on demonstration curation in imitation learning, not model efficiency.
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to general robotics and control, not industrial vision inspection.
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on policy optimization in imitation learning rather than AI safety.
Adaptive-Group Process Rewards for GUI Agents Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets policy optimization efficiency rather than safety or reliability.
Adaptive-Group Process Rewards for GUI Agents Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Develops a training reward framework rather than an evaluation benchmark.
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on ECG waveform classification rather than agent systems.
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies recursive self-improvement on ECGs, not general LLM evaluation.
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on specialized biomedical signal learning rather than deployment safety.
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain self-improvement, not model compression or efficient serving.
Keep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs Presents PolyGraph, a method storing provenance receipts during knowledge graph construction to enable precise updates when source documents are retracted. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on knowledge graph maintenance, not LLM benchmark evaluation.
Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains Analyzes how multi-agent workflow architecture affects the propagation and attenuation of coordinated stance manipulation across sequential agent chains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies belief propagation rather than LLM benchmarking methodologies.
Evidence-First Agentic Verification Introduces Sauron, an evidence-first agentic verifier that checks both environment state and execution traces against original sources to validate task rubrics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on verifier accuracy and reward hacking, not efficient serving.
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on benchmarks but proposes a training framework, not evaluation.
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on capability retention rather than safety guardrails or alignment.
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses continual learning degradation, not model compression or efficiency.
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal inputs like image, video, or audio.
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents a compact classifier, but efficiency is not the primary research focus.
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on malicious skill detection rather than general benchmark evaluation.
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on screening malicious skills rather than software engineering tasks.
SkillScreen: High-Recall Screening of Malicious Agent Skills with Decision Models This paper presents a compact decision model designed to screen malicious agent tool skills with high recall directly from specification files. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or document search.
Safety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants This paper introduces a benchmark to evaluate whether egocentric multimodal assistants understand who is harmed rather than solely tracking output refusal. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multimodal assistant safety rather than agent planning or tools.
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reward optimization rather than model compression or inference efficiency.
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns general RL training rather than safety alignment or guardrails.
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses RL credit assignment, not multi-step agent planning.
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on post-training RL optimization rather than benchmark evaluation.
Explore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning This paper presents a reinforcement learning approach that uses in-context dynamic memory during rollouts and self-distillation to accelerate reasoning exploration. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets reasoning efficiency rather than safety or reliability risks.
Explore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning This paper presents a reinforcement learning approach that uses in-context dynamic memory during rollouts and self-distillation to accelerate reasoning exploration. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a training framework rather than a testing benchmark.
Explore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning This paper presents a reinforcement learning approach that uses in-context dynamic memory during rollouts and self-distillation to accelerate reasoning exploration. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on RLVR exploration and self-distillation rather than agent workflows.
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning This paper introduces a posterior learning approach that improves multi-sample reasoning coverage by updating models using single posterior draws. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an optimization method rather than evaluation methodologies or benchmarks.
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning This paper introduces a posterior learning approach that improves multi-sample reasoning coverage by updating models using single posterior draws. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-sample posterior optimization, not multi-step agent systems.
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning This paper introduces a posterior learning approach that improves multi-sample reasoning coverage by updating models using single posterior draws. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimization efficiency, not safety or guardrails.
DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models? This paper presents a benchmark with an action taxonomy to evaluate the interactive conversation capabilities of full-duplex spoken language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates conversational flow rather than AI safety or reliability.
DuplexAct: How Interactive Are Frontier Full-Duplex Spoken Language Models? This paper presents a benchmark with an action taxonomy to evaluate the interactive conversation capabilities of full-duplex spoken language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on spoken conversational turn-taking, not tool-using or planning agents.
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers causal treatment estimation rather than AI deployment safety or guardrails.
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses statistical covariate dimension reduction, not LLM compression or serving.
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on counterfactual prediction methods rather than LLM evaluation benchmarks.
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with causal inference for longitudinal data, not LLM agents.
Coupled Harness--Model Evolution for Scientific Equation Discovery This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Targets scientific equation fitting rather than AI safety or guardrails.
Coupled Harness--Model Evolution for Scientific Equation Discovery This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a tool-learning method rather than a general LLM evaluation benchmark.
Auditing Failure Attribution: Label Predictability and Judge Behavior This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or document retrieval.
Auditing Failure Attribution: Label Predictability and Judge Behavior This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on auditing LLM evaluation judges rather than multi-step planning agents.
Auditing Failure Attribution: Label Predictability and Judge Behavior This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text-based LLM judges rather than multimodal vision-language architectures.
Auditing Failure Attribution: Label Predictability and Judge Behavior This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines failure attribution cues in benchmark tasks rather than software generation or repair.
Auditing Failure Attribution: Label Predictability and Judge Behavior This paper provides four auditing tests to determine whether failure-attribution judges actually perform multi-step checks or merely rely on spurious surface cues. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, distillation, or serving efficiency.
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical robot teleoperation and imitation learning, not LLM agents.
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses whole-body mobile manipulation rather than industrial visual inspection or defect detection.
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an imitation learning system rather than an LLM evaluation benchmark.
I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses robotic manipulation failures rather than LLM safety, guardrails, or alignment.
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an architectural coordination framework rather than an LLM evaluation benchmark.
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies agent coordination trade-offs rather than safety guardrails or alignment risks.
Degrees of Freedom in Multi-Agent Coordination: Between Centralized and Decentralized Regimes This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates organizational routing between agents rather than model compression or inference efficiency.
Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents This paper introduces an approach and benchmark for evaluating whether language model agents can infer unstated true requirements instead of following incomplete instructions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses instruction completeness and intent discovery rather than safety guardrails or red-teaming.
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback This paper proposes Gaussian process bandit algorithms with selective verification audits to optimize AI system configurations under noisy evaluations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses GP bandit optimization theory rather than neural network compression or serving.
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback This paper proposes Gaussian process bandit algorithms with selective verification audits to optimize AI system configurations under noisy evaluations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical GP bandit verification theory rather than standard LLM benchmarks.
Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback This paper proposes Gaussian process bandit algorithms with selective verification audits to optimize AI system configurations under noisy evaluations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a Bayesian optimization method for audit designs, not LLM agent systems.
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotics and sensor fusion rather than language model planning and tool use.
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D computer vision and LiDAR-thermal fusion rather than vision-language models.
Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an industrial inspection system rather than an LLM benchmark.

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